
"""simple docstring"""


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def 						lowerCamelCase__      (				UpperCAmelCase_ = "laptop"					)->				DataFrame:







       """simple docstring"""
       UpperCamelCase       			=		F"https://www.amazon.in/laptop/s?k={product}"
       UpperCamelCase       			=		{
           "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
           "Accept-Language": "en-US, en;q=0.5",
       }
       UpperCamelCase       			=		BeautifulSoup(requests.get(UpperCAmelCase_		,   headers=UpperCAmelCase_					).text					)
       # Initialize a Pandas dataframe with the column titles
       UpperCamelCase       			=		DataFrame(
           columns=[
               "Product Title",
               "Product Link",
               "Current Price of the product",
               "Product Rating",
               "MRP of the product",
               "Discount",
           ]					)
       # Loop through each entry and store them in the dataframe
       for item, _ in zip_longest(
           soup.find_all(
               "div"		,   attrs={"class": "s-result-item", "data-component-type": "s-search-result"}		,   )		,   soup.find_all("div"		,   attrs={"class": "a-row a-size-base a-color-base"}					)		,   ):
              try:
                     UpperCamelCase       			=		item.ha.text
                     UpperCamelCase       			=		"https://www.amazon.in/" + item.ha.a["href"]
                     UpperCamelCase       			=		item.find("span"		,   attrs={"class": "a-offscreen"}					).text
                     try:
                            UpperCamelCase       			=		item.find("span"		,   attrs={"class": "a-icon-alt"}					).text
                     except AttributeError:
                            UpperCamelCase       			=		"Not available"
                     try:
                            UpperCamelCase       			=		(
                                "₹"
                                + item.find(
                                    "span"		,   attrs={"class": "a-price a-text-price"}					).text.split("₹"					)[1]
                            )
                     except AttributeError:
                            UpperCamelCase       			=		""
                     try:
                            UpperCamelCase       			=		float(
                                (
                                    (
                                        float(product_mrp.strip("₹"					).replace(","		,   ""					)					)
                                        - float(product_price.strip("₹"					).replace(","		,   ""					)					)
                                    )
                                    / float(product_mrp.strip("₹"					).replace(","		,   ""					)					)
                                )
                                * 1_00					)
                     except ValueError:
                            UpperCamelCase       			=		float("nan"					)
              except AttributeError:
                     pass
              UpperCamelCase       			=		[
                  product_title,
                  product_link,
                  product_price,
                  product_rating,
                  product_mrp,
                  discount,
              ]
       UpperCamelCase       			=		" "
       UpperCamelCase       			=		" "
       data_frame.index += 1
       return data_frame


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  """headphones"""
      get_amazon_product_data(product).to_csv(F'''Amazon Product Data for {product}.csv''')







"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





